Adaptive Composition in Dynamic Service Environments
- Submitting institution
-
The University of Essex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1294
- Type
- D - Journal article
- DOI
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10.1016/j.future.2016.12.003
- Title of journal
- Future Generation Computer Systems
- Article number
- -
- First page
- 215
- Volume
- 80
- Issue
- -
- ISSN
- 0167-739X
- Open access status
- Deposit exception
- Month of publication
- December
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
A - Artificial Intelligence (AI)
- Citation count
- 17
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper builds upon the authors' two earlier conference papers, on multi-granularity service composition (which triggered a wave of various papers in a similar direction) and on reactive service selection in dynamic environments (which have over 1000 downloads). The paper substantially extends these two papers with a novel execution-time adaptive behaviour, and extensive theoretical and empirical evaluation. Significantly it was the first to efficiently exploit emerging new opportunities during service execution, which have been neglected previously by state-of-the-art adaptive approaches. The paper has already impacted on the evaluation of adaptive service composition algorithms (Abbassi Imed, 2018).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -